Interpretive Summary: The economic value of rice in domestic and international markets is strongly affected by the eating quality of cooked rice. Conventionally, the eating quality of rice has been assessed by a combination of human preference and physical and chemical property evaluations. These evaluations are typically time-consuming, only assess whether rice has quality characteristics desired by a particular population or for a specific application, and fall short in measuring subtle differences in eating quality. Rapid, accurate, universal methods that relate physical and chemical property measures to objective, human taste analyses are needed. The purpose of this study was to determine the extent amylography (test determining viscosity of cooked rice flour) performed on a rapid visco-analyser (RVA) can be used to predict the textural properties of cooked rice. None of the cooked rice textural properties, whether measured by objective, human taste tests or by instrumental texture analyses, was modeled by RVA with high accuracy. Cohesiveness of mass (the degree to which the rice holds together while chewing) was most accurately predicted (50% of the time). RVA does not appear to hold promise as a stand-alone tool for predicting cooked rice texture. Successful development of a rapid, accurate, universal method will allow rice to be directed to the most appropriate, highest value markets.

Technical Abstract:
Although amylose content is considered to be the most important determinant of cooked rice texture, it is recognized that this constituent falls short as a predictor, because cultivars of similar amylose content may differ in textural properties. The purpose of this study was to determine how well amylography conducted using a rapid visco-analyser (RVA) serves as a predictor of cooked rice texture, alone and in combination with amylose contents. Textural properties of 88 short-, medium-, and long-grain cultivars were assessed by descriptive sensory analysis and instrumental texture profile analysis (TPA) and related to RVA measures. None of the cooked rice textural attributes, whether measured by descriptive analysis or TPA, were modeled by RVA with high accuracy (i.e. high r 2). The sensory texture attributes cohesiveness of mass, stickiness, and initial starchy coating and the TPA attribute adhesiveness were found to have the strongest correlations to RVA measures. Setback explained most of the variance attributed to models describing these attributes; the strongest correlation (r 2 = 0.47) was with cohesiveness of mass. Inclusion of amylose content in the regression analyses did not strengthen the models. When included in the model for cohesiveness of mass, amylose content replaced setback. Amylose content and setback were strongly correlated (r 2=0.76). Exclusion of samples that cook atypical of their amylose contents and/or gelatinization temperature types did not improve the use of RVA measures to predict cooked rice texture.